@misc{Hao2019Blockchain-Based,
  author = {Hao, Kun and Xin, Junchang and Wang, Zhiqiong and Cao, Keyan and Wang, Guoren},
  title = {Blockchain-Based Outsourced Storage Schema in Untrusted Environment},
  journal = {IEEE Access},
  year = {2019},
  volume = {7},
  pages = {122707-122721},
  doi = {10.1109/ACCESS.2019.2938578},
  issn = {2169-3536},
  note = {IEEE Access},
  source = {IEEE Xplore},
  abstract = {Outsourced data, as significant service offered by the cloud service provider (CSP), can effectively facilitate the data owner (DO) overcoming the storage limitations on massive data. To ensure the availability of data, DO usually outsources the data replications to multiple CSPs (multi-CSPs) and utilizes a third party metadata management (TPMM) to dominate the metadata of the corresponding replications. However, during the outsourced procedures, DO can hardly confirm the confidence of the TPMM who may take some malicious behaviors to affect the reliability of data. Thus, DO inevitably faces data security issues caused by the over-reliance on the semi-trusted TPMM to manage the metadata of replications. In this paper, we focus on the problem of reliable outsourced data service among multi-CSPs in untrusted environment, that is, how to reliably store and verify the metadata of the data replications in untrusted multi-CSPs environment. To address the problem, we use the novel blockchain technology as a medium to build a trusted outsourced service platform. Moreover, we fully consider the innovative characteristics of blockchain including decentralized architecture, redundancy storage, collective maintenance, and tamper resistant to ensure the data cannot be changed maliciously. We first design a blockchain-based outsourced service framework for storing data replications in untrusted environment, which contains three key layers, that is, storage layer, verification layer, and blockchain layer. Then, we devise a novel concept of verification peer (VP) for maintaining metadata stored by a form of blockchain, and each of which holds the entire blockchain locally to prevent metadata from being maliciously tampered with. Finally, based on the proposed model, we introduce a collaborative algorithm invoked by VPs to store and verify the metadata of replications. We present a completed analysis and conduct extensive experiments on multi-CSPs scenario. The evaluation results demonstrate that our proposed approach achieves superior performance.},
  keywords = {Metadata;Blockchain;Reliability;Collaboration;Distributed databases;Privacy;Blockchain;collaborative;outsourced data;reliable storage;untrusted environment},
  selection_criteria = {Estudios que no aborden la gestión de datos académicos en contextos educativos},
  created_at = {45542.8304976852},
  updated_at = {45955.6297337963},
  created_by = {laura2910},
  updated_by = {oscar1485},
  status = {Rejected},
  x_title = {Blockchain-Based Outsourced Storage Schema in Untrusted Environment},
  x_author = {Hao, Kun and Xin, Junchang and Wang, Zhiqiong and Cao, Keyan and Wang, Guoren},
  x_year = {2019},
  x_doi = {10.1109/ACCESS.2019.2938578}
}
@misc{Waveren2019An,
  author = {van Waveren, C.C},
  title = {An implementation framework for a quality management system in an academic department at a tertiary educational institution},
  journal = {SAIEE Africa Research Journal},
  year = {2019},
  volume = {96},
  pages = {207-213},
  doi = {10.23919/SAIEE.2005.9488049},
  issn = {1991-1696},
  note = {SAIEE Africa Research Journal},
  source = {IEEE Xplore},
  abstract = {An academic department at a higher educational institution that wishes to provide a customer focused product or service in a consistent way has to put a prominent and official management system in place. Such a system will bring about synergy between academic and supporting functions, and will also link the different academic and support processes to customer and stakeholder requirements through a set of management system requirements. Currently, tertiary education institutions throughout South Africa, as well as in the rest of the world, focus their quality efforts on the quality assurance of academic programs, but not necessarily on the effective and efficient implementation and utilization of all processes that deliver those educational programs. Since the first introduction of the concept of Total Quality Management, as well as the latest international ISO9000:2000 Quality Management System standard, quality has moved away from the traditional focus of assurance to that of ensuring quality through the effective and efficient management of all business processes. As a step to move from quality assurance to ensuring quality, a holistic implementation framework for a quality management system, based on the ISO9000:2000 series of quality management system standards is proposed that will enable an academic department at a higher education institution to provide a customer focused product or service in a consistent way, by focusing on the core business processes that deliver the service.},
  keywords = {Quality management;Standards;Stakeholders;Quality assurance;Business;Qualifications;Industries;Education;Management;Education management;Quality assurance;Quality control},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.8329861111},
  updated_at = {45544.981412037},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {An implementation framework for a quality management system in an academic department at a tertiary educational institution},
  x_author = {van Waveren, C.C},
  x_year = {2019},
  x_doi = {10.23919/SAIEE.2005.9488049}
}
@misc{Bode2024Toward,
  author = {Bode, Jan and Kühl, Niklas and Kreuzberger, Dominik and Holtmann, Carsten},
  title = {Toward Avoiding the Data Mess: Industry Insights From Data Mesh Implementations},
  journal = {IEEE Access},
  year = {2024},
  volume = {12},
  pages = {95402-95416},
  doi = {10.1109/ACCESS.2024.3417291},
  issn = {2169-3536},
  note = {IEEE Access},
  source = {IEEE Xplore},
  abstract = {With the increasing importance of data and artificial intelligence, organizations strive to become more data-driven. However, current data architectures are not necessarily designed to keep up with the scale and scope of data and analytics use cases. In fact, existing architectures often fail to deliver the promised value associated with them. Data mesh is a socio-technical, decentralized, distributed concept for enterprise data management. As the concept of data mesh is still novel, it lacks empirical insights from the field. Specifically, an understanding of the motivational factors for introducing data mesh, the associated challenges, implementation strategies, its business impact, and potential archetypes is missing. To address this gap, we conduct 15 semi-structured interviews with industry experts. Our results show, among other insights, that organizations have difficulties with the transition toward federated data governance associated with the data mesh concept, the shift of responsibility for the development, provision, and maintenance of data products, and the comprehension of the overall concept. In our work, we derive multiple implementation strategies and suggest organizations introduce a cross-domain steering unit, observe the data product usage, create quick wins in the early phases, and favor small dedicated teams that prioritize data products. Whereas we acknowledge that organizations need to apply implementation strategies according to their individual needs, we also deduct two archetypes that provide suggestions in more detail. Our findings synthesize insights from industry experts and provide researchers and professionals with preliminary guidelines for the successful adoption of data mesh.},
  keywords = {Organizations;Big Data applications;Data warehouses;Data governance;Standards organizations;Information systems;Database systems;Artificial intelligence;Big data;data governance;data mesh;management information systems},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.8256828704},
  updated_at = {45544.6533101852},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {Toward Avoiding the Data Mess: Industry Insights From Data Mesh Implementations},
  x_author = {Bode, Jan and Kühl, Niklas and Kreuzberger, Dominik and Holtmann, Carsten},
  x_year = {2024},
  x_doi = {10.1109/ACCESS.2024.3417291}
}
@misc{Rustemi2023A,
  author = {Rustemi, Avni and Dalipi, Fisnik and Atanasovski, Vladimir and Risteski, Aleksandar},
  title = {A Systematic Literature Review on Blockchain-Based Systems for Academic Certificate Verification},
  journal = {IEEE Access},
  year = {2023},
  volume = {11},
  pages = {64679-64696},
  doi = {10.1109/ACCESS.2023.3289598},
  issn = {2169-3536},
  note = {IEEE Access},
  source = {IEEE Xplore},
  abstract = {In the past few years, there has been significant progress made in the area of blockchain. The use of blockchain technology has the potential to revolutionize the educational system by providing individuals with innovative and cost-effective ways to learn, as well as by altering the way teachers and students work together. Additionally, blockchain technology can be utilized for the issuing of unchangeable digital certificates, and it can enhance the present limitations of the existing certificate verification systems by making them quicker, more reliable, and independent of the central authority. The application of blockchain in the context of education has generated significant scientific interest in this field. Nonetheless, research endeavors on the adoption of blockchain in the verification of academic credentials are still in the development phase. In order to shed more light on the field, in this paper we focus on extensively reviewing the body of knowledge on blockchain-based systems for academic certificate verification. Hence, the purpose of this survey is to compile all relevant research into a systematic literature review, highlighting the key contributions from various researchers throughout the years with a focus on the past, present, and future. In this work, we have identified 34 relevant studies out of 1744 papers that were published between 2018 and 2022 by employing the PRISMA framework. We distinguished six major themes covered by the research articles analyzed and also identified research gaps that need to be addressed and explored by the research community. Based on the findings of this review, we provide some recommendations for future research directions and practical applications that can assist researchers, policymakers, and practitioners in the field.},
  keywords = {Blockchains;Education;Systematics;Security;Smart contracts;Bibliographies;Forgery;Blockchain platforms;smart contracts;academic certificate verification;systematic literature review;security and transparency;fraud prevention;ethereum;automatic certificate generation},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.8304976852},
  updated_at = {45544.654537037},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {A Systematic Literature Review on Blockchain-Based Systems for Academic Certificate Verification},
  x_author = {Rustemi, Avni and Dalipi, Fisnik and Atanasovski, Vladimir and Risteski, Aleksandar},
  x_year = {2023},
  x_doi = {10.1109/ACCESS.2023.3289598}
}
@misc{Funde2022Big,
  author = {Funde, Snehalata and Swain, Gandharba},
  title = {Big Data Privacy and Security Using Abundant Data Recovery Techniques and Data Obliviousness Methodologies},
  journal = {IEEE Access},
  year = {2022},
  volume = {10},
  pages = {105458-105484},
  doi = {10.1109/ACCESS.2022.3211304},
  issn = {2169-3536},
  note = {IEEE Access},
  source = {IEEE Xplore},
  abstract = {The concept of big data security is introduced in this article along with many features. It illustrates the need for security in healthcare systems as the volume of data increases continuously over the period of time. The necessity of big data security as well as several big data analytics phases highlighted. It covers many big data privacy-preserving strategies. Many digital storage solutions being used in today’s world are designed to work only with fixed format of the data. This paper introduces some methods for maintaining metadata obliviousness. The oblivious RAM technology mentioned in the research article address security concerns and it can be handled with the daily increase in data in several industries. Security needs are introduced at many phases of big data creation, such as information extraction, storage systems, and analytics of the information. Additionally, it presents several data recovery methods for recovering original data in the event of a data crash. This paper covers several data categorization methods for sorting data into normal and sensitive categories as well as methods for anomaly detection. It discusses the advantages and disadvantages of various security measures.},
  keywords = {Cloud computing;Security;Big Data;Safety;Medical services;Internet of Things;Privacy;Data governance;Random access memory;Metadata;Information retrieval;Digital storage;Computer crashes;Security;privacy;obliviousness;data recovery},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.7724189815},
  updated_at = {45544.6489467593},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {Big Data Privacy and Security Using Abundant Data Recovery Techniques and Data Obliviousness Methodologies},
  x_author = {Funde, Snehalata and Swain, Gandharba},
  x_year = {2022},
  x_doi = {10.1109/ACCESS.2022.3211304}
}
@misc{Lee2019Big,
  author = {Lee, Doyoung},
  title = {Big Data Quality Assurance Through Data Traceability: A Case Study of the National Standard Reference Data Program of Korea},
  journal = {IEEE Access},
  year = {2019},
  volume = {7},
  pages = {36294-36299},
  doi = {10.1109/ACCESS.2019.2904286},
  issn = {2169-3536},
  note = {IEEE Access},
  source = {IEEE Xplore},
  abstract = {In the era of big data, the scientific and social demand for quality data is aggressive and urgent. This paper sheds light on the expanded role of metrology of verifying validated procedures of data production and developing adequate uncertainty evaluation methods to ensure the trustworthiness of data and information. In this regard, I explore the mechanism of the national standard reference data (SRD) program of Korea, which connects various scientific and social sectors to metrology by applying useful metrological concepts and methods to produce reliable data and convert such data into national standards. In particular, the changing interpretation of metrological key concepts, such as “measurement,” “traceability,” and “uncertainty,” will be explored and reconsidered from the perspective of data quality assurance. As a result, I suggest the concept of “data traceability” with “the matrix of data quality evaluation” according to the elements of a data production system and related evaluation criteria. To conclude, I suggest social and policy implications for the new role of metrology and standards for producing and disseminating reliable knowledge sources from big data.},
  keywords = {Standards;Uncertainty;Big Data;Metrology;Reliability;Measurement uncertainty;Biomedical measurement;Big data;data quality;data traceability;metrology;standard reference data;uncertainty},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.8329861111},
  updated_at = {45544.6616782407},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {Big Data Quality Assurance Through Data Traceability: A Case Study of the National Standard Reference Data Program of Korea},
  x_author = {Lee, Doyoung},
  x_year = {2019},
  x_doi = {10.1109/ACCESS.2019.2904286}
}
@misc{Tardo2020An,
  author = {Tardío, Roberto and Maté, Alejandro and Trujillo, Juan},
  title = {An Iterative Methodology for Defining Big Data Analytics Architectures},
  journal = {IEEE Access},
  year = {2020},
  volume = {8},
  pages = {210597-210616},
  doi = {10.1109/ACCESS.2020.3039455},
  issn = {2169-3536},
  note = {IEEE Access},
  source = {IEEE Xplore},
  abstract = {Thanks to the advances achieved in the last decade, the lack of adequate technologies to deal with Big Data characteristics such as Data Volume is no longer an issue. Instead, recent studies highlight that one of the main Big Data issues is the lack of expertise to select adequate technologies and build the correct Big Data architecture for the problem at hand. In order to tackle this problem, we present our methodology for the generation of Big Data pipelines based on several requirements derived from Big Data features that are critical for the selection of the most appropriate tools and techniques. Thus, thanks to our approach we reduce the required know-how to select and build Big Data architectures by providing a step-by-step methodology that leads Big Data architects into creating their Big Data Pipelines for the case at hand. Our methodology has been tested in two use cases.},
  keywords = {Big Data;Pipelines;Data models;Proposals;Computer architecture;Big Data applications;Tools;Big data pipelines;business intelligence;data management;Hadoop;NoSQL},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.8304976852},
  updated_at = {45544.6588888889},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {An Iterative Methodology for Defining Big Data Analytics Architectures},
  x_author = {Tardío, Roberto and Maté, Alejandro and Trujillo, Juan},
  x_year = {2020},
  x_doi = {10.1109/ACCESS.2020.3039455}
}
@misc{Jia2021Optimized,
  author = {Jia, Dayu and Xin, Junchang and Wang, Zhiqiong and Wang, Guoren},
  title = {Optimized Data Storage Method for Sharding-Based Blockchain},
  journal = {IEEE Access},
  year = {2021},
  volume = {9},
  pages = {67890-67900},
  doi = {10.1109/ACCESS.2021.3077650},
  issn = {2169-3536},
  note = {IEEE Access},
  source = {IEEE Xplore},
  abstract = {COVID-19 virus is raging across the planet. In countries where the epidemic is under control, the main mode of virus transmission is through the transport of imported refrigerated food from epidemic areas. Blockchain is a great way for the government to trace every piece of food. However, the high-performance requirements of the blockchain system for nodes limit its wide application. Several sharding-based blockchain systems have been proposed to solve this limitation. Which blocks should be saved by nodes in the sharding-based blockchain system is a new problem. To solve this problem, the optimized data storage method is proposed in this paper. Five features of block popularity are presented, including the objective feature of a block, the objective feature of the block associated with the node, the historical popularity, the hidden popularity and the storage requirements. Then the ELM classifier is used in the optimized model due to its high performance of training and classification. Finally, the experimental results on synthetic data demonstrate the accuracy and efficiency of the optimized data storage model.},
  keywords = {Blockchain;Peer-to-peer computing;Supply chains;Viruses (medical);Memory;Data models;Pain;Blockchain;hot block;classification;sharding technology;extreme learning machine},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.8304976852},
  updated_at = {45544.6558217593},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {Optimized Data Storage Method for Sharding-Based Blockchain},
  x_author = {Jia, Dayu and Xin, Junchang and Wang, Zhiqiong and Wang, Guoren},
  x_year = {2021},
  x_doi = {10.1109/ACCESS.2021.3077650}
}
@misc{Bozkurt2023Toward,
  author = {Bozkurt, Yusuf and Rossmann, Alexander and Konanahalli, Ashwini and Pervez, Zeeshan},
  title = {Toward Urban Data Governance: Status-Quo, Challenges, and Success Factors},
  journal = {IEEE Access},
  year = {2023},
  volume = {11},
  pages = {85656-85677},
  doi = {10.1109/ACCESS.2023.3302835},
  issn = {2169-3536},
  note = {IEEE Access},
  source = {IEEE Xplore},
  abstract = {The benefits of urban data cannot be realized without a political and strategic view of data use. A core concept within this view is data governance, which aligns strategy in data-relevant structures and entities with data processes, actors, architectures, and overall data management. Data governance is not a new concept and has long been addressed by scientists and practitioners from an enterprise perspective. In the urban context, however, data governance has only recently attracted increased attention, despite the unprecedented relevance of data in the advent of smart cities. Urban data governance can create semantic compatibility between heterogeneous technologies and data silos and connect stakeholders by standardizing data models, processes, and policies. This research provides a foundation for developing a reference model for urban data governance, identifies challenges in dealing with data in cities, and defines factors for the successful implementation of urban data governance. To obtain the best possible insights, the study carries out qualitative research following the design science research paradigm, conducting semi-structured expert interviews with 27 municipalities from Austria, Germany, Denmark, Finland, Sweden, and the Netherlands. The subsequent data analysis based on cognitive maps provides valuable insights into urban data governance. The interview transcripts were transferred and synthesized into comprehensive urban data governance maps to analyze entities and complex relationships with respect to the current state, challenges, and success factors of urban data governance. The findings show that each municipal department defines data governance separately, with no uniform approach. Given cultural factors, siloed data architectures have emerged in cities, leading to interoperability and integrability issues. A city-wide data governance entity in a cross-cutting function can be instrumental in breaking down silos in cities and creating a unified view of the city’s data landscape. The further identified concepts and their mutual interaction offer a powerful tool for developing a reference model for urban data governance and for the strategic orientation of cities on their way to data-driven organizations.},
  keywords = {Data governance;Smart cities;Data models;Stakeholders;Interviews;Organizations;Urban areas;Local government;Cognitive mapping;data governance;design science research;urban data governance;smart city;expert interviews},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.8256828704},
  updated_at = {45544.6517361111},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {Toward Urban Data Governance: Status-Quo, Challenges, and Success Factors},
  x_author = {Bozkurt, Yusuf and Rossmann, Alexander and Konanahalli, Ashwini and Pervez, Zeeshan},
  x_year = {2023},
  x_doi = {10.1109/ACCESS.2023.3302835}
}
@misc{Zhang2021Research,
  author = {Zhang, Airong and Lv, Na},
  title = {Research on the Impact of Big Data Capabilities on Government’s Smart Service Performance: Empirical Evidence From China},
  journal = {IEEE Access},
  year = {2021},
  volume = {9},
  pages = {50523-50537},
  doi = {10.1109/ACCESS.2021.3056486},
  issn = {2169-3536},
  note = {IEEE Access},
  source = {IEEE Xplore},
  abstract = {The government of China seeks to improve e-government service quality and build a service-oriented government that citizens find satisfactory. To this end, big data is being used as a new tool of government service innovation. However, there is a lack of research on how big data affects the performance of government smart services. This article explores the influence mechanisms of government big data capabilities on the performance of smart service provision, utilizing the carding analysis of relevant literature, published both in China and abroad. To this end, a structural equation model was constructed. Using data from 289 valid questionnaires in Jiangsu, Shandong, Zhejiang, and other provinces and cities in China, the study tests internal mechanisms of big data capabilities and its effect on smart service performance. Following a new definition of government big data capability, the paper divides the capability into three dimensions: big data system capability, big data human capability and big data management capability. The main conclusions are as follows: (1) Big data management capability has a significant positive impact on big data human capability and big data system capability. (2) Big data system capability has a significant positive impact on big data human capability. (3) Big data system capability and big data management capability have a significant positive effect on smart service performance. (4) The impact of big data human capability on smart service performance is not however significant enough to bring about the improvements which the government seeks.},
  keywords = {Big Data;Government;Decision making;Data models;Mathematical model;Technological innovation;Information technology;Big data system capabilities;big data human capabilities;big data management capabilities;smart service performance;structural equation model},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.7724189815},
  updated_at = {45544.6503587963},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {Research on the Impact of Big Data Capabilities on Government’s Smart Service Performance: Empirical Evidence From China},
  x_author = {Zhang, Airong and Lv, Na},
  x_year = {2021},
  x_doi = {10.1109/ACCESS.2021.3056486}
}
@misc{You2020A,
  author = {You, Xindong and Lv, Xueqiang and Zhao, Zhikai and Han, Junmei and Ren, Xueping},
  title = {A Survey and Taxonomy on Energy-Aware Data Management Strategies in Cloud Environment},
  journal = {IEEE Access},
  year = {2020},
  volume = {8},
  pages = {94279-94293},
  doi = {10.1109/ACCESS.2020.2992748},
  issn = {2169-3536},
  note = {IEEE Access},
  source = {IEEE Xplore},
  abstract = {During the past ten years, the energy consumption problem in cloud-related environments has attracted substantial attention in research and industrial communities. Researchers have conducted many surveys on energy efficiency issues from different perspectives. All of the surveys can be classified into five categories: surveys on the energy efficiency of the whole cloud related system, surveys on the energy efficiency of a certain level or component of the cloud, surveys on all of the energy efficient strategies, surveys on a certain energy efficiency techniques, and other energy efficiency related surveys. However, to the best of our knowledge, surveys on energy-aware data management strategies in cloud-related environment are absent. In this paper, we conduct a comprehensive survey on energy saving-aware data management strategies in cloud-related environments, such as data classification, data placement and data replication strategies. Compared to current existing reviews on energy efficiency in cloud-related environments, we firstly conduct the survey on the energy consumption problem from the data management perspective. Furthermore, we classify the energy-aware data management strategies from different perspectives. This survey and the taxonomy of the energy-aware data management strategies demonstrate the potential for reducing the energy consumption at the data management level of a cloud storage system, which will compress more space for energy reduction and finally achieve energy proportionality. Moreover, this survey and taxonomy on the energy efficiency issue from the data management perspective is an important supplement to current existing surveys on energy efficiency in cloud-related environments.},
  keywords = {Cloud computing;Energy consumption;Taxonomy;Data centers;Green products;Layout;Ocean temperature;Energy consumption;cloud storage system;data classification;data placement;data replication;energy proportionality},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.7724189815},
  updated_at = {45544.6454398148},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {A Survey and Taxonomy on Energy-Aware Data Management Strategies in Cloud Environment},
  x_author = {You, Xindong and Lv, Xueqiang and Zhao, Zhikai and Han, Junmei and Ren, Xueping},
  x_year = {2020},
  x_doi = {10.1109/ACCESS.2020.2992748}
}
@misc{Singh2019Decision,
  author = {Singh, Jatinder and Cobbe, Jennifer and Norval, Chris},
  title = {Decision Provenance: Harnessing Data Flow for Accountable Systems},
  journal = {IEEE Access},
  year = {2019},
  volume = {7},
  pages = {6562-6574},
  doi = {10.1109/ACCESS.2018.2887201},
  issn = {2169-3536},
  note = {IEEE Access},
  source = {IEEE Xplore},
  abstract = {Demand is growing for more accountability regarding the technological systems that increasingly occupy our world. However, the complexity of many of these systems—often systems-of-systems—poses accountability challenges. A key reason for this is because the details and nature of the information flows that interconnect and drive systems, which often occur across technical and organizational boundaries, tend to be invisible or opaque. This paper argues that data provenance methods show much promise as a technical means for increasing the transparency of these interconnected systems. Specifically, given the concerns regarding ever-increasing levels of automated and algorithmic decision-making, and so-called “algorithmic systems” in general, we propose decision provenance as a concept showing much promise. Decision provenance entails using provenance methods to provide information exposing decision pipelines: chains of inputs to, the nature of, and the flow-on effects from the decisions and actions taken (at design and run-time) throughout systems. This paper introduces the concept of decision provenance, and takes an interdisciplinary (tech-legal) exploration into its potential for assisting accountability in algorithmic systems. We argue that decision provenance can help facilitate oversight, audit, compliance, risk mitigation, and user empowerment, and we also indicate the implementation considerations and areas for research necessary for realizing its vision. More generally, we make the case that considerations of data flow, and systems more broadly, are important to discussions of accountability, and complement the considerable attention already given to algorithmic specifics.},
  keywords = {Law;Decision making;Art;Process control;Data protection;Accountability;AI;algorithmic & automated decision-making;data management;GDPR;governance;IoT;law;machine learning;privacy;provenance;security;systems of systems;transparency},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.7699189815},
  updated_at = {45544.642037037},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {Decision Provenance: Harnessing Data Flow for Accountable Systems},
  x_author = {Singh, Jatinder and Cobbe, Jennifer and Norval, Chris},
  x_year = {2019},
  x_doi = {10.1109/ACCESS.2018.2887201}
}
@misc{Paik2019Analysis,
  author = {Paik, Hye-Young and Xu, Xiwei and Bandara, H. M. N. Dilum and Lee, Sung Une and Lo, Sin Kuang},
  title = {Analysis of Data Management in Blockchain-Based Systems: From Architecture to Governance},
  journal = {IEEE Access},
  year = {2019},
  volume = {7},
  pages = {186091-186107},
  doi = {10.1109/ACCESS.2019.2961404},
  issn = {2169-3536},
  note = {IEEE Access},
  source = {IEEE Xplore},
  abstract = {In a blockchain-based system, data and the consensus-based process of recording and updating them over distributed nodes are central to enabling the trustless multi-party transactions. Thus, properly understanding what and how the data are stored and manipulated ultimately determines the degree of utility, performance, and cost of a blockchain-based application. While blockchains enhance the quality of the data by providing a transparent, immutable, and consistent data store, the technology also brings new challenges from a data management perspective. In this paper, we analyse blockchains from the viewpoint of a developer to highlight important concepts and considerations when incorporating a blockchain into a larger software system as a data store. The work aims to increase the level of understanding of blockchain technology as a data store and to promote a methodical approach in applying it to large software systems. First, we identify the common architectural layers of a typical software system with data stores and conceptualise each layer in blockchain terms. Second, we examine the placement and flow of data in blockchain-based applications. Third, we explore data administration aspects for blockchains, especially as a distributed data store. Fourth, we discuss the analytics of blockchain data and trustable data analytics enabled by blockchain. Lastly, we examine the data governance issues in blockchains in terms of privacy and quality assurance.},
  keywords = {Blockchain;Distributed databases;Smart contracts;Computer architecture;Software systems;Unified modeling language;Analytics;blockchain;databases;data governance;data handling;distributed data management;distributed databases;software architecture;transaction databases},
  selection_criteria = {Estudios disponibles de forma gratuita o a través de bases de datos accesibles},
  created_at = {45542.7500810185},
  updated_at = {45544.6402430556},
  created_by = {laura2910},
  updated_by = {laura2910},
  status = {Accepted},
  x_title = {Analysis of Data Management in Blockchain-Based Systems: From Architecture to Governance},
  x_author = {Paik, Hye-Young and Xu, Xiwei and Bandara, H. M. N. Dilum and Lee, Sung Une and Lo, Sin Kuang},
  x_year = {2019},
  x_doi = {10.1109/ACCESS.2019.2961404}
}
